The loss in expectation of life after colon cancer: a population-based study

Income disparities in loss in life expectancy after colon and rectal cancers: a Swedish register-based study

BACKGROUND

Differences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We quantified the lifetime impact of CRC and its variation by SEP, using individualised income to conceptualise SEP.

METHODS

Data included all adults with a first-time diagnosis of colon or rectal cancers in Sweden between 2008 and 2021. The analysis was done separately for colon and rectal cancers using flexible parametric models. For each cancer and income group, we estimated the life expectancy in the absence of cancer, the life expectancy in the presence of cancer and the loss in life expectancy (LLE).

RESULTS

We found large income disparities in life expectancy after a cancer diagnosis, with larger differences among the youngest patients. Higher income resulted in more years lost following a cancer diagnosis. For example, 40-year-old females with colon cancer lost 17.64 years if in the highest-income group and 13.68 years if in the lowest-income group. Rectal cancer resulted in higher LLE compared with colon cancer. Males lost a larger proportion of their lives. All patients, including the oldest, lost more than 30% of their remaining life expectancy. Based on the number of colon and rectal cancer diagnoses in 2021, colon cancer results in almost double the number of years lost compared with rectal cancer (24 669 and 12 105 years, respectively).

CONCLUSION

While our results should be interpreted in line with what individualised income represents, they highlight the need to address inequalities.

Increase in the Life Expectancy of Patients with Cancer in the United States

BACKGROUND

Cancer is becoming more of a chronic disease due to improvements in treatment and early detection for multiple cancer sites. To gain insight on increased life expectancy due to these improvements, we quantified trends in the loss in expectation of life (LEL) due to a cancer diagnosis for six cancer sites from 1975 through 2018.

METHODS

We focused on patients diagnosed with female breast cancer, chronic myeloid leukemia (CML), colon and rectum cancer, diffuse large B-cell lymphoma (DLBCL), lung cancer, or melanoma between 1975 and 2018 from nine Surveillance, Epidemiology, and End Results cancer registries. Life expectancies for patients with cancer ages 50+ were modeled using flexible parametric survival models. LEL was calculated as the difference between general population life expectancy and life expectancy for patients with cancer.

RESULTS

Over 2 million patients were diagnosed with one of the six cancers between 1975 and 2018. Large increases in life expectancy were observed between 1990 and 2010 for female breast, DLBCL, and CML. Patients with colon and rectum cancer and melanoma had more gradual improvements in life expectancy. Lung cancer LEL only began decreasing after 2005. Increases in life expectancy corresponded with decreases in LEL for patients with cancer.

CONCLUSIONS

The reported gains in life expectancy largely correspond to progress in the screening, management, and treatment of these six cancers since 1975.

IMPACT

LEL provides an important public health perspective on how improvements in treatment and early detection and their impacts on survival translate into changes in cancer patients' life expectancy.

Reference adjusted loss in life expectancy for population-based cancer patient survival comparisons - with an application to colon cancer in Sweden

BACKGROUND

The loss in life expectancy, LLE, is defined as the difference in life expectancy between cancer patients and that of the general population. It is a useful measure for summarising the impact of a cancer diagnosis on an individual's life expectancy. However, it is less useful for making comparisons of cancer survival across groups or over time, since the LLE is influenced by both mortality due to cancer and other causes and the life expectancy in the general population.

METHODS

We present an approach for making LLE estimates comparable across groups and over time by using reference expected mortality rates with flexible parametric relative survival models. The approach is illustrated by estimating temporal trends in LLE of colon cancer patients in Sweden.

RESULTS

The life expectancy of Swedish colon cancer patients has improved, but the LLE has not decreased to the same extent since the life expectancy in the general population has also increased. When using a fixed population and other-cause mortality, i.e. a reference-adjusted approach, the LLE decreases over time. For example, using 2010 mortality rates as the reference, the LLE for females diagnosed at age 65 decreased from 11.3 if diagnosed in 1976 to 7.2 if diagnosed in 2010, and from 3.9 to 1.9 years for women 85 years old at diagnosis.

CONCLUSION

The reference-adjusted LLE is useful for making comparisons across calendar time, or groups, since differences in other cause mortality are removed.

IMPACT

The reference-adjusted approach enhances the use of LLE as a comparative measure.

Quantifying Differences in Remaining Life Expectancy after Cancer Diagnosis, Aboriginal and Torres Strait Islanders, and Other Australians, 2005-2016

BACKGROUND

This study quantified differences in remaining life expectancy (RLE) among Aboriginal and Torres Strait Islander and other Australian patients with cancer. We assessed how much of this disparity was due to differences in cancer and noncancer mortality and calculated the population gain in life years for Aboriginal and Torres Strait Islanders cancer diagnoses if the cancer survival disparities were removed.

METHODS

Flexible parametric relative survival models were used to estimate RLE by Aboriginal and Torres Strait Islander status for a population-based cohort of 709,239 persons (12,830 Aboriginal and Torres Strait Islanders), 2005 to 2016.

RESULTS

For all cancers combined, the average disparity in RLE was 8.0 years between Aboriginal and Torres Strait Islanders (12.0 years) and other Australians (20.0 years). The magnitude of this disparity varied by cancer type, being >10 years for cervical cancer versus <2 years for lung and pancreatic cancers. For all cancers combined, around 26% of this disparity was due to differences in cancer mortality and 74% due to noncancer mortality. Among 1,342 Aboriginal and Torres Strait Islanders diagnosed with cancer in 2015 an estimated 2,818 life years would be gained if cancer survival disparities were removed.

CONCLUSIONS

A cancer diagnosis exacerbates the existing disparities in RLE among Aboriginal and Torres Strait Islanders. Addressing them will require consideration of both cancer-related factors and those contributing to noncancer mortality.

IMPACT

Reported survival-based measures provided additional insights into the overall impact of cancer over a lifetime horizon among Aboriginal and Torres Strait Islander peoples.

Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000 to 2018: A population-based cohort study

BACKGROUND

The provision of different types of mortality metrics (e.g., mortality rate ratios [MRRs] and life expectancy) allows the research community to access a more informative set of health metrics. The aim of this study was to provide a panel of mortality metrics associated with a comprehensive range of disorders and to design a web page to visualize all results.

METHODS AND FINDINGS

In a population-based cohort of all 7,378,598 persons living in Denmark at some point between 2000 and 2018, we identified individuals diagnosed at hospitals with 1,803 specific categories of disorders through the International Classification of Diseases-10th Revision (ICD-10) in the National Patient Register. Information on date and cause of death was obtained from the Registry of Causes of Death. For each of the disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and differences in life expectancy (estimated as life years lost [LYLs]). Additionally, we examined models that adjusted for measures of air pollution to explore potential associations with MRRs. We focus on 39 general medical conditions to simplify the presentation of results, which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer. A total of 3,676,694 males and 3,701,904 females were followed up for 101.7 million person-years. During the 19-year follow-up period, 1,034,273 persons (14.0%) died. For 37 of the 39 selected medical conditions, mortality rates were larger and life expectancy shorter compared to the Danish general population. For these 37 disorders, MRRs ranged from 1.09 (95% confidence interval [CI]: 1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while LYLs ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. Adjustment for air pollution had very little impact on the estimates; however, a limitation of the study is the possibility that the association between the different disorders and mortality could be explained by other underlying factors associated with both the disorder and mortality.

CONCLUSIONS

In this study, we show estimates of incidence, age of onset, age of death, and mortality metrics (both MRRs and LYLs) for a comprehensive range of disorders. The interactive data visualization site (https://nbepi.com/atlas) allows more fine-grained analysis of the link between a range of disorders and key mortality estimates.

Cancer death and potential years of life lost in Feicheng City, China: Trends from 2013 to 2018

This study aimed to evaluate the impact of cancer-related mortality on life expectancy in Feicheng City.We extracted the death records and population data of Feicheng City from 2013 to 2018 through the Feicheng Center for Disease Control and Prevention. The mortality, premature mortality, cause-eliminated life expectancy, potential years of life lost (PYLL), average potential years of life lost (APYLL), annual change percentage (APC), and other indicators of cancer were calculated. The age-standardized rates were calculated using the sixth national census (2010).From 2013 to 2018, the mortality rate of cancer in Feicheng City was 221.55/100,000, and the standardized mortality rate was 166.37/100,000. The standardized mortality rate increased from 2013 to 2014 and then decreased annually. The premature mortality of cancer was 8.98% and showed a downward trend (APC = -2.47%, t = -3.10, P = .04). From 2013 to 2018, the average life expectancy of residents in Feicheng City was 78.63 years. Eliminating the impact of cancer, life expectancy could increase by 3.72 years. The rate of life loss caused by cancer in men was higher than that in women. The total life loss caused by cancer deaths was 126,870.50 person-years, the potential life loss rate was 22.51‰, and the average potential life loss was 13.30 years. The standardized potential years of life lost rate showed a downward trend (APC = -2.96%, t = -3.72, P = .02), and APYLL decreased by 1.98% annually (t = -5.44, P = .01). The top 5 malignant tumors in APYLL were leukemia, breast cancer, brain tumor, liver cancer, and ovarian cancer.Lung cancer, esophageal cancer, female breast cancer, and childhood leukemia have a great impact on the life expectancy of residents in Feicheng City. Effective measures need to be taken to reduce the disease burden of malignant tumors.

Temporal improvements noted in life expectancy of patients with colorectal cancer; a Dutch population-based study

OBJECTIVE

Specific survival estimates are needed for the increasing number of colorectal cancer (CRC) survivors. The aim of this population-based study was to determine conditional loss in expectation of life (LEL) due to CRC.

STUDY DESIGN AND SETTING

All surgically treated patients with CRC registered in the Netherlands Cancer Registry with stage I-III between 1990-2016, were included (N = 203,216). Estimates of conditional LEL were predicted using flexible parametric models and the total life years lost due to cancer were estimated.

RESULTS

LEL decreased with older age and patients with rectal cancer or higher disease stage had highest LEL. In 2010, LEL for sixty-year old male and female patients was 2 vs. 2, 4 vs. 4, and 7 vs. 8 years for colon cancer, and 2 vs. 2, 4 vs. 5 and 7 vs. 8 years for rectal cancer, respectively. Conditional LEL in patients with CRC decreased during follow-up. Patients with combined stage I-III colon and rectal cancer in 2010 lost an estimated 18,628 and 11,336 life years.

CONCLUSION

This study quantified the impact of CRC on patient's life expectancy, both on individual and population level and demonstrated temporal improvements in CRC survival. These results provide meaningful information that can be used during follow-up.

Understanding the impact of sex and stage differences on melanoma cancer patient survival: a SEER-based study

BACKGROUND

This paper investigates the difference in survival of melanoma patients across stage and sex by utilising net survival measures. Metrics are presented at both the individual and population level.

METHODS

Flexible parametric models were fitted to estimate life-expectancy metrics to be applied to a group of 104,938 subjects with a melanoma skin cancer diagnosis from 2000 to 2017. Period analysis was used for better predictions for newly diagnosed patients, and missing-stage information was imputed for 9918 patients. Female relative survival was assigned to male subjects to demonstrate the survival discrepancies experienced between sexes.

RESULTS

At the age of 60, males diagnosed at the regional stage lose an average of 4.99 years of life compared to the general population, and females lose 4.79 years, demonstrating the sex variation in expected mortality. In 2017, males contributed 3545 more life years lost than females, and a potential 1931 life years could be preserved if sex differences in survival were eliminated.

CONCLUSIONS

This study demonstrates the survival differences across population subgroups as a result of a melanoma cancer diagnosis. Females experience better prognosis across age and stage at diagnosis; however, further investigation is necessary to better understand the mechanisms behind this difference.

Cancer survival statistics for patients and healthcare professionals - a tutorial of real-world data analysis

Monitoring survival of cancer patients using data collected by population-based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real-world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population-based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons.

Spatio-temporal analysis of colorectal cancer using a geographic information system in the Iranian military community during the period 2007-2016

INTRODUCTION

Colorectal cancer (CRC) is one of the most prevalent cancers among Iranian people. The study of spatio-temporal distribution of disease has an important role in the design of disease prevention programmes. The purpose of the current study was to describe the spatio-temporal distribution of CRC in the Iranian military community as a sample of the Iranian population.

METHODS

In the current ecological study, all registered cancer cases in the Iranian military community during the period 2007-2016 were considered. To identify hotspots, Getis-Ord Gi statistics were used. All analyses were performed using ArcGIS 10.5 and Excel 2010.

RESULTS

The highest incidences of CRC in 2007-2008, 2009-2010 and 2011-2012 were recorded in Kermanshah province. The highest incidences of CRC in 2013-2014 were seen in Kermanshah, Ghilan, Tehran and North Khorasan. In 2007-2008 and 2009-2010, hotspots were detected in West Azarbayjan. In 2011-2012, hotspots were detected in Zanjan and Qazvin. In 2013-2014, a hotspot was detected in Qazvin. Finally, West Azerbaijan was the hotspot for CRC in 2015-2016.

CONCLUSIONS

The incidence of CRC in men was higher than in women. Also it appeared that North and North West Iran were risk areas for this disease, and so these areas should be considered in the design of disease prevention programme for this cancer type. Additionally, the determination of individual risk factors in the aforementioned geographical areas can play an important role in the prevention of this type of cancer.

CircFNDC3B sequestrates miR-937-5p to derepress TIMP3 and inhibit colorectal cancer progression

Circular RNA (circRNA) are single‐stranded RNA with covalently closed 3′ and 5′ ends, with many recognized to be involved in human diseases as gene regulators, typically by interacting with other RNA. CircFNDC3B is a circRNA formed by back‐splicing of exons 5 and 6 of the FNDC3B gene. CircFNDC3B was recently implicated in renal carcinoma, gastric and bladder cancer. However, the expression levels of circFNDC3B and its role in colorectal cancer (CRC) remain unclear. Expression of circFNDC3B and TIMP3 levels in CRC tissues and cell lines were found to be low, whereas microRNA (miR)‐937‐5p expression was high in CRC. MicroRNA‐937‐5p downregulated TIMP3, thereby promoting tumor cell proliferation, invasion, migration and angiogenesis. Moreover, CircFNDC3B was shown to bind to miR‐937‐5p. CircFNDC3B and circFNDC3B‐enriched exosomes inhibited tumorigenic, metastatic and angiogenic properties of CRC, and miR‐937‐5p overexpression or TIMP3 knockdown could reverse these effects. In vivo CRC tumor growth, angiogenesis and liver metastasis were suppressed by circFNDC3B overexpression, circFNDC3B‐enriched exosomes or miR‐937‐5p knockdown. In conclusion, our work reports a tumor‐suppressing role for the circFNDC3B–miR‐97‐5p–TIMP3 pathway and suggests that circFNDC3B‐enriched exosomes can inhibit angiogenesis and CRC progression.

Does minimum follow-up time post-diagnosis matter? An assessment of changing loss of life expectancy for people with cancer in Western Australia from 1982 to 2016

BACKGROUND

Cancer survival has improved in Western Australia (WA) over recent decades. Loss of life expectancy (LOLE) is a useful measure for assessing cancer survival at a population-level. Some previous studies estimating LOLE have required a minimum follow-up beyond diagnosis to reduce the impact of modelled extrapolation, while others have not. The first aim of this study was to assess the impact of minimum length of follow-up on LOLE estimates for people diagnosed in 2006 with female breast, colorectal, prostate, lung, cervical, combined oesophageal and stomach cancers, and melanoma. Based on these results, the second aim was to assess temporal changes in LOLE for these cancer types for diagnoses between 1982 and 2016.

METHODS

Person-level linked cancer registry and mortality data were used for invasive primary cancer diagnoses for WA residents aged 15-89 years. The analysis for aim one included cases diagnosed from 1982 to the end of 2006, followed to the end of 2006 (i.e. no minimum follow-up), 2011 (i.e. five years minimum follow-up, assuming survival) or 2016 (i.e. 10 years minimum follow-up). To achieve the second study aim, the diagnostic period was extended to the end of 2016. Life expectancy estimates were obtained after fitting flexible parametric relative survival models. Single-year age and sex-specific death rates were used as a reference to estimate LOLE and proportionate loss of life expectancy.

RESULTS

Temporal changes were not reported for prostate, cervical, oesophageal and stomach cancers or melanoma, due to differences in LOLE estimates by minimum follow-up time, or estimate imprecision. Marked reductions in LOLE were observed for female breast and colorectal cancer. There was minimal absolute reduction for lung cancer, where LOLE remained high.

CONCLUSION

This study considered the appropriateness of including recent cancer diagnoses when assessing temporal changes in LOLE, finding variation in estimates with differing minimum follow-up or high parameter uncertainty for most included cancer types. Temporal changes in LOLE in-turn reflected changes in the life expectancy of the general population, cancer detection and management. These factors must be considered when estimating and interpreting LOLE estimates.

On estimating the time to statistical cure

BACKGROUND

The mortality risk among cancer patients measured from the time of diagnosis is often elevated in comparison to the general population. However, for some cancer types, the patient mortality risk will over time reach the same level as the general population mortality risk. The time point at which the mortality risk reaches the same level as the general population is called the cure point and is of great interest to patients, clinicians, and health care planners. In previous studies, estimation of the cure point has been handled in an ad hoc fashion, often without considerations about margins of clinical relevance.

METHODS

We review existing methods for estimating the cure point and discuss new clinically relevant measures for quantifying the mortality difference between cancer patients and the general population, which can be used for cure point estimation. The performance of the methods is assessed in a simulation study and the methods are illustrated on survival data from Danish colon cancer patients.

RESULTS

The simulations revealed that the bias of the estimated cure point depends on the measure chosen for quantifying the excess mortality, the chosen margin of clinical relevance, and the applied estimation procedure. These choices are interdependent as the choice of mortality measure depends both on the ability to define a margin of clinical relevance and the ability to accurately compute the mortality measure. The analysis of cancer survival data demonstrates the importance of considering the confidence interval of the estimated cure point, as these may be wide in some scenarios limiting the applicability of the estimated cure point.

CONCLUSIONS

Although cure points are appealing in a clinical context and has widespread applicability, estimation remains a difficult task. The estimation relies on a number of choices, each associated with pitfalls that the practitioner should be aware of.

lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition

Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available.

The Loss in Expectation of Life due to Early-Onset Mild Cognitive Impairment and Early-Onset Dementia in Norway

INTRODUCTION

Whether patients with early-onset dementia have poorer or improved survival compared with those with a late onset largely depends on the survival measure. Survival estimates for early-onset mild cognitive impairment (MCI) diagnosis are particularly scarce. We aimed to estimate life expectancy (LE) in patients with early-onset dementia or early MCI, and loss in expectation of life (LEL) for these groups. Comparisons were made with the general Norwegian population and a subgroup of patients with late-onset dementia.

METHODS

Early onset was defined as receiving a diagnosis of MCI or dementia before age 65 years. LE and LEL were predicted using flexible parametric survival models. Our study population was comprised of newly diagnosed (incident) cases (n = 4,906), aged 50-90 years at the time of diagnosis (672 were diagnosed before age 65 years, of which 291 were diagnosed with dementia), in the Norwegian register of persons assessed for cognitive symptoms (NorCog) between 2009 and 2017, and patients were followed up for mortality or censorship until January 2018.

RESULTS

Among the early-onset patients, 8 and 23% died during follow-up, in the MCI and dementia groups, respectively. Both early-onset MCI and especially early-onset dementia were associated with lower LE than in the general Norwegian population; LE for 60-year-old women in 2016 was 26 years in the general population, 20 years in MCI patients, and 7 years in dementia patients. The corresponding LE at 80 years was 10, 6, and 5 years. Thus, LEL were particularly pronounced for patients with early dementia. The diagnosis-specific LE pattern in men was similar to that in women.

DISCUSSION

Early-onset MCI was associated with substantial life years lost (5-6 years), but the loss was particularly pronounced for those with early-onset dementia, reducing the expected life length by 2 decades.

Generalized parametric cure models for relative survival

Cure models are used in time-to-event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Although numerous cure models have been proposed in the statistical literature, there is no consensus on how to formulate these. We introduce a general parametric formulation of mixture cure models and a new class of cure models, termed latent cure models, together with a general estimation framework and software, which enable fitting of a wide range of different models. Through simulations, we assess the statistical properties of the models with respect to the cure proportion and the survival of the uncured individuals. Finally, we illustrate the models using survival data on colon cancer, which typically display a plateau in the relative survival. As demonstrated in the simulations, mixture cure models which are not guaranteed to be constant after a finite time point, tend to produce accurate estimates of the cure proportion and the survival of the uncured. However, these models are very unstable in certain cases due to identifiability issues, whereas LC models generally provide stable results at the price of more biased estimates.

Changes in life expectancy for cancer patients over time since diagnosis

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Research question: how cancer impacts on LE changes during patients’ entire life

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LE increased in patients surviving the first years and decreasing thereafter.

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Patients’ LE in the long-term approached but seldom reached the general population’s LE.

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This method describes when cancer survivors’ excess risk of death became negligible.

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Life expectancy indicator is easy to be understood and interpreted by patients.

The aims of this study were to provide life expectancy (LE) estimates of cancer patients at diagnosis and LE changes over time since diagnosis to describe the impact of cancer during patients' entire lives. Cancer patients' LE was calculated by standard period life table methodology using the relative survival of Italian patients diagnosed in population-based cancer registries in 1985–2011 with follow-up to 2013. Data were smoothed using a polynomial model and years of life lost (YLL) were calculated as the difference between patients' LE and that of the age- and sex-matched general population. The YLL at diagnosis was highest at the youngest age at diagnosis, steadily decreasing thereafter. For patients diagnosed at age 45 years, the YLL was above 20 for lung and ovarian cancers and below 6 for thyroid cancer in women and melanoma in men. LE progressively increased in patients surviving the first years, decreasing thereafter, to approach that of the general population. YLL in the long run mainly depends on attained age. Providing quantitative data is essential to better define clinical follow-up and plan health care resource allocation. These results help assess when the excess risk of death from tumour becomes negligible in cancer survivors.

Loss in life expectancy and gain in life years as measures of cancer impact

There are a broad range of survival-based metrics that are available to report from cancer survival studies, with varying advantages and disadvantages. A combination of metrics should be considered to improve comprehensibility and give a fuller understanding of the impact of cancer. In this article, we discuss the utility of loss in life expectancy and gain in life years as measures of cancer impact, and to quantify differences across population groups. These measures are simple to interpret, have a real-world meaning, and evaluate impact over a life-time horizon. We illustrate the use of the loss in life expectancy measures through a range of examples using data on women diagnosed with cancer in England. We use four different examples across a number of tumour types to illustrate different uses of the metrics, and highlight how they can be interpreted and used in practice in population-based oncology studies. Extensions of the measures conditional on survival to specific times after diagnosis can be used to give updated prognosis for cancer patients. Furthermore, we show how the measures can be used to understand the impact of population differences seen across patient groups. We believe that these under-used, and relatively easy to calculate, measures of overall impact can supplement reporting of cancer survival metrics and improve the comprehensibility compared to the metrics typically reported.

Estimating the loss of lifetime function using flexible parametric relative survival models

Background

Within cancer care, dynamic evaluations of the loss in expectation of life provides useful information to patients as well as physicians. The loss of lifetime function yields the conditional loss in expectation of life given survival up to a specific time point. Due to the inevitable censoring in time-to-event data, loss of lifetime estimation requires extrapolation of both the patient and general population survival function. In this context, the accuracy of different extrapolation approaches has not previously been evaluated.

Methods

The loss of lifetime function was computed by decomposing the all-cause survival function using the relative and general population survival function. To allow extrapolation, the relative survival function was fitted using existing parametric relative survival models. In addition, we introduced a novel mixture cure model suitable for extrapolation. The accuracy of the estimated loss of lifetime function using various extrapolation approaches was assessed in a simulation study and by data from the Danish Cancer Registry where complete follow-up was available. In addition, we illustrated the proposed methodology by analyzing recent data from the Danish Lymphoma Registry.

Results

No uniformly superior extrapolation method was found, but flexible parametric mixture cure models and flexible parametric relative survival models seemed to be suitable in various scenarios.

Conclusion

Using extrapolation to estimate the loss of lifetime function requires careful consideration of the relative survival function outside the available follow-up period. We propose extensive sensitivity analyses when estimating the loss of lifetime function.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0661-8) contains supplementary material, which is available to authorized users.

Carbon Dioxide Insufflation Increases Colonoscopic Adenoma Detection Rate Compared With Air Insufflation

GOALS

To determine the effect of carbon dioxide insufflation on the most important outcome measure of colonoscopic quality: adenoma detection rate (ADR).

BACKGROUND

Bowel cancer is the second most common cause of cancer deaths in males and females in Australia. Carbon dioxide has in recent times become the insufflation methodology of choice for screening colonoscopy for bowel cancer, as this has been shown to have significant advantages when compared with traditional air insufflation.

STUDY

Endoscopies performed over a period of 9 months immediately before and after the implementation of carbon dioxide insufflation at endoscopy centers were eligible for inclusion.

RESULTS

The difference in ADR between the carbon dioxide and air insufflation methods was statistically significant, with an increased ADR in the carbon dioxide group. The superiority of carbon dioxide insufflation was sustained with a logistic regression model, which showed ADR was significantly impacted by insufflation method.

CONCLUSIONS

Carbon dioxide insufflation is known to reduce abdominal pain, postprocedural duration of abdominal pain, abdominal distension, and analgesic requirements. This study represents for the first time the beneficial effect of carbon dioxide insufflation upon the key quality colonoscopy indicator of ADR.

Long-term survival and loss in expectancy of life in a population-based cohort of 7114 patients with diffuse large B-cell lymphoma

Survival has improved among patients with diffuse large B-cell lymphoma (DLBCL) with the addition of anti-CD20 antibody therapy. We aimed to quantify trends and remaining loss in expectation of life (LEL) due to DLBCL at a national population-based level. Patients diagnosed with DLBCL 2000-2013 (N = 7114) were identified through the Swedish Lymphoma Registry and classified according to the age-adjusted International Prognostic Index (aaIPI). The novel measure LEL is the difference between remaining life years among patients and the general population and was predicted using flexible parametric models from diagnosis and among 2-year survivors, by age and sex. Median age at DLBCL-diagnosis was 70 (18-105) years and 54.8% presented with stage III-IV disease. On average, LEL due to DLBCL decreased from 8.0 (95% CI: 7.7-8.3) to 4.6 (95% CI: 4.5-4.6) years over the study period. By risk group, LEL was most reduced among patients with aaIPI ≥2 aged 50-60 years. However, these patients were still estimated to lose >8 years in 2013 (eg, LELmales50years 8.6 years (95% CI: 5.0-12.3)). Among 2-year survivors, LEL was reduced from 6.1 years (95% CI: 5.6-6.5) (aaIPI ≥ 2) and 3.8 years (95% CI: 3.6-4.1) (aaIPI < 2) to 1.1 (95% CI: 1.1-1.2) and 1.0 year (95% CI: 0.8-1.1), respectively. The reduction was observed across all ages. Results for females were similar. By using LEL we illustrate the improvement of DLBCL survival over time. Despite adequate immunochemotherapy, substantial LEL among patients with IPI ≥ 2 points to remaining unmet medical needs. We speculate that observed reduced losses among 2-year survivors indicate a reduction of late relapses.

Estimating the impact of a cancer diagnosis on life expectancy by socio-economic group for a range of cancer types in England

BACKGROUND

Differences in cancer survival exist across socio-economic groups for many cancer types. Standard metrics fail to show the overall impact for patients and the population.

METHODS

The available data consist of a population of ∼2.5 million patients and include all patients recorded as being diagnosed with melanoma, prostate, bladder, breast, colon, rectum, lung, ovarian and stomach cancers in England between 1998 and 2013. We estimated the average loss in expectation of life per patient in years and the proportion of life lost for a range of cancer types, separately by deprivation group. In addition, estimates for the total number of years lost due to each cancer were also obtained.

RESULTS

Lung and stomach cancers result in the highest overall loss for males and females in all deprivation groups in terms of both absolute life years lost and loss as a proportion of expected life remaining. Female lung cancer patients in the least- and most-deprived group lose 14.4 and 13.8 years on average, respectively, that is translated as 86.1% and 87.3% of their average expected life years remaining. Melanoma, prostate and breast cancers have the lowest overall loss. On the basis of the number of patients diagnosed in 2013, lung cancer results in the most life years lost in total followed by breast cancer. Melanoma and bladder cancer account for the lowest total life years lost.

CONCLUSIONS

There are wide differences in the impact of cancer on life expectancy across deprivation groups, and for most cancers the most affluent lose less years.

Endoscopic Improvement of the Adenoma Detection Rate during Colonoscopy - Where Do We Stand in 2015?

BACKGROUND

The presence of colorectal adenomas is considered a major risk factor for colorectal cancer development. The implementation of screening colonoscopy programs in the Western world has led to a substantial reduction of colorectal cancer death. Many efforts have been made to reduce the adenoma miss rates by the application of new endoscopic devices and techniques for better adenoma visualization.

SUMMARY

This special review gives the readership an overview of current endoscopic innovations that can aid in the increase of the adenoma detection rate (ADR) during colonoscopy. These innovations include the use of devices like EndoCuff® and EndoRings® as well as new technical equipment like third-eye endoscope® and full-spectrum endoscopy (FUSE®).

KEY MESSAGE

Technical improvements and newly developed accessories are able to improve the ADR. However, additional costs and a willingness to invest into potentially expensive equipment might be necessary. Investigator-dependent skills remain the backbone in the ADR detection.